Automatic Landmark-Based Segmentation of Human Subcortical Structures in MRI

2026-05-13

AI Summary

This study addresses the discrepancy between automatic segmentation and expert manual delineation of deep brain structures in MRI by proposing an anatomical landmark-guided 3D segmentation method. The approach explicitly integrates anatomical landmarks into the deep segmentation pipeline for the first time, employing a global-to-local network to accurately detect 16 key landmarks. These landmarks are then combined with 3D semantic segmentation and landmark-driven post-processing to refine 12 coarse labels into 26 fine-grained structures. By incorporating local anatomical constraints through this strategy, the method substantially improves boundary precision, yielding automated segmentations that align more closely with the manual segmentation protocol defined by the Harvard–Oxford atlas.

Abstract

Precise segmentation of brain structures in magnetic resonance imaging (MRI) is essential for reliable neuroimaging analysis, yet voxel-wise deep models often yield anatomically inconsistent results that diverge from expert-defined boundaries. In this research, we propose a landmark-guided 3D brain segmentation approach that explicitly mimics the manual segmentation protocol of the Harvard--Oxford Atlas. A Global-to-Local network automatically detects 16 landmarks representing key subcortical reference points. Then, a semantic segmentation model produces a coarse segmentation of 12 anatomical labels, each grouping multiple subcortical regions. Finally, a landmark-driven post-processing step separates these 12 labels into 26 distinct structures by enforcing local anatomical constraints. Experimental results demonstrate consistent improvements in boundary accuracy. Overall, integrating learned landmarks aligns segmentations more closely with manual protocols.

Problem

• MRI segmentation
• subcortical structures
• anatomical consistency
• landmark-based segmentation
• neuroimaging analysis

Innovation

• landmark-guided segmentation
• subcortical structures
• anatomical consistency
• Harvard-Oxford Atlas
• MRI brain segmentation